Anthropic's Model Naming, Extrapolated · Sam Wilkinson

With the release of Claude Fable, it is clear that Anthropic is progressing from poems to enterprise-scale narrative objects.

If Claude Fable stops helping you, you'll never know — Jonathon Ready

Anthropic's Claude Fable policy turns coding assistants into non-neutral infrastructure for product companies.

AI didn’t break the web. The dotcons did – AI just turned up the volume – #OMN (Open Media Network)

Google Employees Internally Share Memes About How Its AI Sucks

Google’s CEO says 75% of the company’s code is AI-generated. The people who write that code say the AI they’re using is overhyped.

Mental health: although screens can play a negative role, more than 6 out of 10 people turn to artificial intelligence for psychological support.

Mental health: although screens can play a negative role, more than 6 out of 10 people turn to artificial intelligence for psychological support.

Arint - SEO+KI (@[email protected])

<p>RT @haider1: Der Grund, warum Anthropic "Mythos" noch immer im Labor eingesperrt hält:</p> <p><a href="https://arint.info/@Arint/116655871166811283">mehr</a> auf <a href="https://arint.info/">Arint.info</a></p> <p>#AI #AIHumor #Anthropic #MachineLearning #Mythos #arint_info</p> <p><a href="https://x.com/haider1/status/2059721299250098262#m">https://x.com/haider1/status/2059721299250098262#m</a></p>

Language Models Need Sleep

Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. To handle this, we study a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache. During sleep, the model performs $N$ offline recurrent passes over the accumulated context and updates the fast weights in its state-space model (SSM) blocks through a learned local rule. During inference, this shifts extra computation to sleep while preserving the latency of wake-time prediction. We test our method on controlled synthetic tasks, including cellular automata and multi-hop graph retrieval, as well as a realistic math reasoning task, on which a regular transformer as well as SSM-attention hybrid models fail. We then show that increasing sleep duration $N$ for our models improves performance, with the largest gains on examples that require deeper reasoning.

Don't just 'quote' the AI

If your reply is a wall of unedited LLM junk, you are the problem.